Log-linear parameterizations in discriminant analysis

Abstract

Applications of log-linear models in discrete discriminant analysis usually treat the grouping variable as a variable in the model. An alternative parameterization is introduced which models the association structure between variables for each population separately. The separate log-linear models may have differing complexity. It is shown that these approaches lead to different classes of models. Applications to the choice of car brand and credit scoring show the usefulness of separate modelling.